97 citations · 234 across the 26 of their papers we have counts for
6 papers · 1 filter
Asymptotic Consistency of Loss-Calibrated Variational Bayes
Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao
This paper establishes the asymptotic consistency of the {\it loss-calibrated variational Bayes} (LCVB) method. LCVB was proposed in~\cite{LaSiGh2011} as a method for approximately…
Community detection over a heterogeneous population of non-aligned networks
Guilherme Gomes, Vinayak Rao, Jennifer Neville
Clustering and community detection with multiple graphs have typically focused on aligned graphs, where there is a mapping between nodes across the graphs (e.g., multi-view, multi-…
An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions
Qi Wang, Vinayak Rao, Yee Whye Teh
Stochastic differential equations (SDEs) or diffusions are continuous-valued continuous-time stochastic processes widely used in the applied and mathematical sciences. Simulating p…
On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making
Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao
We study data-driven decision-making problems in the Bayesian framework, where the expectation in the Bayes risk is replaced by a risk-sensitive entropic risk measure. We focus on…
Relational Pooling for Graph Representations
Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao +1
This work generalizes graph neural networks (GNNs) beyond those based on the Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and diffusions. Our approach, denoted Relational Po…
Asymptotic Consistency of Rényi-Approximate Posteriors
Prateek Jaiswal, Vinayak A. Rao, Harsha Honnappa
We study the asymptotic consistency properties of -Rényi approximate posteriors, a class of variational Bayesian methods that approximate an intractable Bayesian posterior with…